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* add instructblip * minicpm_v * remove <image> from qwen-vl * speed up postprocessing * Optimize build context speed --------- Co-authored-by: Pu Fanyi <FPU001@e.ntu.edu.sg> Co-authored-by: kcz358 <kaichenzhang358@outlook.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,230 @@ | ||
| import torch | ||
| import logging | ||
| import copy | ||
| from tqdm import tqdm | ||
| from lmms_eval import utils | ||
| from lmms_eval.api.instance import Instance | ||
| from lmms_eval.api.model import lmms | ||
| from lmms_eval.api.registry import register_model | ||
| from accelerate import Accelerator, DistributedType | ||
| from accelerate.state import AcceleratorState | ||
| from typing import List, Optional, Union, Tuple | ||
| from transformers import InstructBlipProcessor, InstructBlipForConditionalGeneration | ||
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| from lmms_eval.utils import stop_sequences_criteria | ||
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| import warnings | ||
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| warnings.filterwarnings("ignore") | ||
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| eval_logger = logging.getLogger("lmms-eval") | ||
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| @register_model("instructblip") | ||
| class InstructBLIP(lmms): | ||
| """ | ||
| InstructBLIP Model | ||
| """ | ||
|
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| def __init__( | ||
| self, | ||
| pretrained: str = "Salesforce/instructblip-vicuna-7b", | ||
| device: Optional[str] = "cuda", | ||
| dtype: Optional[Union[str, torch.dtype]] = "auto", | ||
| batch_size: Optional[Union[int, str]] = 1, | ||
| **kwargs, | ||
| ) -> None: | ||
| super().__init__() | ||
| # Do not use kwargs for now | ||
| assert kwargs == {}, f"Unexpected kwargs: {kwargs}" | ||
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| accelerator = Accelerator() | ||
| if accelerator.num_processes > 1: | ||
| self._device = torch.device(f"cuda:{accelerator.local_process_index}") | ||
| else: | ||
| self._device = device | ||
| self._model = InstructBlipForConditionalGeneration.from_pretrained(pretrained,device_map=self._device) | ||
| self._image_processor = InstructBlipProcessor.from_pretrained(pretrained) | ||
| self._tokenizer = self._image_processor.tokenizer | ||
| self._config = self._model.config | ||
| self.model.eval() | ||
| self.model.tie_weights() | ||
| self.batch_size_per_gpu = int(batch_size) | ||
| if accelerator.num_processes > 1: | ||
| assert accelerator.distributed_type in [ | ||
| DistributedType.FSDP, | ||
| DistributedType.MULTI_GPU, | ||
| DistributedType.DEEPSPEED | ||
| ], "Unsupported distributed type provided. Only DDP and FSDP are supported." | ||
| # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model | ||
| # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works | ||
| # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. | ||
| if accelerator.distributed_type == DistributedType.DEEPSPEED: | ||
| kwargs = { | ||
| "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, | ||
| "train_batch_size" : self.batch_size_per_gpu * accelerator.num_processes, | ||
| } | ||
| AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) | ||
| eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") | ||
| if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: | ||
| self._model = accelerator.prepare(self.model) | ||
| else: | ||
| self._model = accelerator.prepare_model(self.model, evaluation_mode=True) | ||
| self.accelerator = accelerator | ||
| if self.accelerator.is_local_main_process: | ||
| eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") | ||
| self._rank = self.accelerator.local_process_index | ||
| self._world_size = self.accelerator.num_processes | ||
| else: | ||
| self.model.to(self._device) | ||
| self._rank = 0 | ||
| self._word_size = 1 | ||
|
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||
| @property | ||
| def config(self): | ||
| # return the associated transformers.AutoConfig for the given pretrained model. | ||
| return self._config | ||
|
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| @property | ||
| def tokenizer(self): | ||
| return self._tokenizer | ||
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| @property | ||
| def model(self): | ||
| # returns the model, unwrapping it if using Accelerate | ||
| if hasattr(self, "accelerator"): | ||
| return self.accelerator.unwrap_model(self._model) | ||
| else: | ||
| return self._model | ||
|
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| @property | ||
| def eot_token_id(self): | ||
| # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* | ||
| return self.tokenizer.eos_token_id | ||
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| @property | ||
| def max_length(self): | ||
| return self._max_length | ||
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| @property | ||
| def batch_size(self): | ||
| return self.batch_size_per_gpu | ||
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| @property | ||
| def device(self): | ||
| return self._device | ||
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| @property | ||
| def rank(self): | ||
| return self._rank | ||
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| @property | ||
| def world_size(self): | ||
| return self._world_size | ||
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| def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: | ||
| """ """ | ||
| add_special_tokens = False if add_special_tokens is None else add_special_tokens | ||
| encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) | ||
| # left-truncate the encoded context to be at most `left_truncate_len` tokens long | ||
| if left_truncate_len: | ||
| encoding = encoding[-left_truncate_len:] | ||
| return encoding | ||
|
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| def tok_decode(self, tokens): | ||
| return self.tokenizer.decode(tokens) | ||
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| def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: | ||
| # TODO | ||
| assert False, "We have not implemented this function for InstructBLIP yet" | ||
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| def loglikelihood_rolling(self, requests: List[Instance]) -> List[float]: | ||
| # TODO | ||
| assert False, "We have not implemented this function for InstructBLIP yet" | ||
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| def flatten(self, input): | ||
| new_list = [] | ||
| for i in input: | ||
| for j in i: | ||
| new_list.append(j) | ||
| return new_list | ||
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| def generate_until(self, requests: List[Instance]) -> List[str]: | ||
| res = [] | ||
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| def _collate(x): | ||
| # the negative sign on len(toks) sorts descending - this has a few advantages: | ||
| # - time estimates will always be over not underestimates, which is more useful for planning | ||
| # - to know the size of a batch when going through the list, you know the first one is always the batch | ||
| # padded context length. this is useful to simplify the batching logic and more importantly to make | ||
| # automatic adaptive batches much much easier to implement | ||
| # - any OOMs will happen right away rather than near the end | ||
| toks = self.tok_encode(x[0]) | ||
| return -len(toks), x[0] | ||
|
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| # we group requests by their generation_kwargs, | ||
| # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling | ||
| # in the same batch. | ||
| re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) | ||
| chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) | ||
| num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 | ||
| pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") | ||
| for chunk in chunks: | ||
| contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk) | ||
| task = task[0] | ||
| split = split[0] | ||
| visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] | ||
| visuals = self.flatten(visuals) | ||
| # we assume all gen kwargs in the batch are the same | ||
| # this is safe to assume because the `grouper` object ensures it. | ||
| gen_kwargs = all_gen_kwargs[0] | ||
|
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| # Set default values for until and max_new_tokens | ||
| until = [self.tok_decode(self.eot_token_id)] | ||
|
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| # Update values from gen_kwargs if present | ||
| if "until" in gen_kwargs: | ||
| until = gen_kwargs.pop("until") | ||
| if isinstance(until, str): | ||
| until = [until] | ||
| elif not isinstance(until, list): | ||
| raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") | ||
| assert self.batch_size_per_gpu == 1, "Do not support batch_size_per_gpu > 1 for now" | ||
| context = contexts[0] | ||
| if "<image>" in context: | ||
| # instruct blip does not expect the <image> tag | ||
| context = context.replace("<image>", "") | ||
| inputs = self._image_processor(images=visuals, text=context, return_tensors="pt").to(self.device) | ||
|
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| gen_kwargs["image_sizes"] = [visuals[idx].size for idx in range(len(visuals))] | ||
| if "max_new_tokens" not in gen_kwargs: | ||
| gen_kwargs["max_new_tokens"] = 1024 | ||
| if "temperature" not in gen_kwargs: | ||
| gen_kwargs["temperature"] = 0 | ||
| if "top_p" not in gen_kwargs: | ||
| gen_kwargs["top_p"] = None | ||
| if "num_beams" not in gen_kwargs: | ||
| gen_kwargs["num_beams"] = 1 | ||
| try: | ||
| cont = self.model.generate( | ||
| **inputs, | ||
| do_sample=True if gen_kwargs["temperature"] > 0 else False, | ||
| temperature=gen_kwargs["temperature"], | ||
| top_p=gen_kwargs["top_p"], | ||
| num_beams=gen_kwargs["num_beams"], | ||
| max_new_tokens=gen_kwargs["max_new_tokens"], | ||
| ) | ||
| except Exception as e: | ||
| eval_logger.error(f"Error {e} in generating") | ||
| cont = "" | ||
| text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip() | ||
| res.append(text_outputs) | ||
| self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs) | ||
| pbar.update(1) | ||
| # reorder this group of results back to original unsorted form | ||
| res = re_ords.get_original(res) | ||
|
|
||
| pbar.close() | ||
| return res |
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